There is considerable activity in the art of fractional distillation to improve the operation of a column so that products with desired purity specifications can be produced for minimum operating costs. Optimizing the operation of a distillation column is complicated, however, because of the column's numerous degrees of freedom, which are characterized as independent input variables, some of which, such as feed temperature and reboiler heat flow, are controllable, while others such as ambient temperature and feed composition are uncontrollable.
High purity distillation has long been recognized as presenting one of the most difficult control problems in the petroleum and chemical processing industries. This is because high purity distillation columns are extremely nonlinear and exhibit extreme nonlinear coupling. As an example of the nonlinear nature of high purity distillation, a 10% increase in boilup rate will result in only a moderate increase in the purity of the bottoms product, while a 10% decrease would cause a drastic decrease in the purity of the bottoms product. In addition, a 10% increase in boilup by itself will cause a drastic reduction in the purity of the overhead product.
High purity distillation, however, is an industrially relevant process because it is used to produce high purity feed stocks for processes that must have such feed stocks in order to operate properly and economically. For example, ethylene, propylene and styrene monomers of nearly 100 percent purity are required for their respective polymerization processes in order to produce polymers with the desired characteristics. Also, for the production of industrial grade acetic acid, levels of less than 200 ppm propanoic acid impurity must be maintained. In addition, chemical intermediate xylene products are typically produced as high purity products. Ethylene oxide and propylene oxide are separated industrially to produce products, each with about 200 ppm impurities.
One of the more important independent variables of a distillation column is the reboiler heat input. This is because distillation is a thermal process, and reboiler heat input may be manipulated to compensate for disturbances in other uncontrollable variables, such as changes in feed composition and feed flow rate. Reflux flow rate is another important independent variable affecting separation which may also be manipulated to compensate for disturbances in the uncontrollable variables. A control system having capability for simultaneously manipulating two variables, such as heat input and reflux flow rate, so as to maintain dual product specifications would be highly desirable.
Industry mainly relies on the classical proportional-integral-derivative (PID) controller or else a linear model based controller for multivariable control applications. Model based controllers, however, provide improved performance over PID controllers in many control applications because it is feasible to automatically update the model to match changing process operating conditions. This permits maintaining near optimum tuning over wide ranges of a process variable. Further, nonlinear models can account for both the nonlinearities of the column and the interactions between manipulated variables of the column to improve control performance compared to systems employing linear models.
It has been demonstrated, however, that dual composition control of a fractional distillation column can be achieved by implementing a nonlinear process model based control (PMBC) strategy wherein tray-to-tray calculations essentially corresponding to the McCabe-Thiele analysis are used to compute liquid and vapor flow rates required for controlling the distillation column. The computer based process model for the PMBC, which is periodically updated within the control strategy, is a steady state approximate model utilizing a mechanistic tray-to-tray calculation, which applies equilibrium and mass balance relationships existing in the column to compute effective control actions. The model is updated based on measured steady state process variables to keep pace with changing operating conditions. The process model is implemented in a supervisory computer control scheme where variables such as the column reflux to distillate flow ratio and the reboiled vapor to bottoms flow ratio are manipulated by the model based controller to maintain desired compositions for distillate and bottoms product streams.
In implementing PMBC, target compositions, which will rapidly return dual product streams to desired compositions, are periodically computed using a generic model control equation which includes proportional, integral and derivative tuning factors. The process model is solved for multiple process flow rates which will satisfy the computed target compositions. The solution includes an update mode for determining an overall tray efficiency of the column by estimating initial values of tray efficiency, calculating tray-to-tray compositions in the stripping section and in the rectifying sections based on the estimated efficiency and measured steady state vapor rates, and then iteratively selecting trial efficiencies which cause a tray composition error between stripping and rectifying section calculations to converge to an acceptable value. This composition error at the feed tray, which is herein referred to as a convergence error, reconciles composition calculations between rectifying and stripping sections of the column. In a similar manner, the model solution includes a control action mode in which a required vapor rate is determined by estimating initial values for vapor rate and calculating tray-to-tray compositions based on estimated vapor rates and the tray efficiency determined in the update mode, and then iteratively selecting vapor rates to achieve a desired convergence error. Accordingly, two general calculation steps are involved in implementing PMBC:
(1) Model parameter update which selects tray efficiency to be adjusted based on measured steady state flow rates and compositions from the column, and PA1 (2) control action calculations utilizing the adjusted tray efficiency to determine set points for flow rates external to the column which serve as manipulated variables.
While nonlinear model based control for binary distillation has proven effective for simultaneously moving dual product compositions toward their respective set points, considerations for realistic control limitations are necessary to maintain control effectiveness. Such realistic considerations include possible constraints on control action moves such as the column vapor rate being constrained by limited heat input or heat removal, pump limiting, etc. Also attention to tuning of computer algorithms such as the generic model control equation, which is embedded in the distillation model, must be realistically considered.
Accordingly it is an object of this invention to improve control of a distillation column by using a nonlinear process model which is effectively tuned for dual product composition control.
It is another object of this invention to operate a distillation column within control action constraints while utilizing nonlinear model based control strategy.